MCP Servers Fbl

🚀 MCP (Model Context Protocol): A Universal AI Interface Protocol

MCP (Model Context Protocol) is a universal AI interface protocol, similar to USB - C. It aims to connect AI models with real - time structured knowledge, expand their capabilities through custom tool execution, and enhance agent combination capabilities.

🚀 Quick Start

MCP serves as an interoperability layer that allows external AI applications (such as Claude, Cursor, etc.) to interact with structured data sources and executable functions. The MCP server can be connected to various AI systems as an external device, enabling the function of "develop once, use everywhere". The MCP client, embedded in the AI host, is responsible for one - to - one interaction with the server via the MCP protocol.

✨ Features

Agent Combination Capability

The MCP architecture supports multi - layer agent interaction, where an AI agent can act as both an MCP client and a server simultaneously. This design allows specialized modules to handle different tasks independently, enabling more complex operations.

Self - Evolving AI through API Registration

Future iterations may introduce dynamic tool discovery functionality, allowing AI clients to register and discover new MCP capabilities in real - time. This will make the system more flexible and capable of automatically expanding its functions according to requirements.

Development and Debugging Support

Use Anthropic's MCP Inspector for interactive testing and debugging without a full deployment environment. This is very useful for rapid development and problem troubleshooting.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

# Example: Simple MCP server - side implementation
class MCPServer:
    def __init__(self):
        self.tools = []
        self.resources = []
        self.prompts = []

    def register_tool(self, tool):
        self.tools.append(tool)
        print(f"Tool registered: {tool}")

    def get_resource(self, resource_id):
        for res in self.resources:
            if res["id"] == resource_id:
                return res
        return None

    def handle_prompt(self, prompt):
        # Logic to handle prompts
        pass

# Example: Simple MCP client implementation
class MCPClient:
    def __init__(self, server):
        self.server = server

    def call_tool(self, tool_id):
        for tool in self.server.tools:
            if tool["id"] == tool_id:
                return tool["function"]()
        return None

    def query_resource(self, resource_id):
        return self.server.get_resource(resource_id)

    def send_prompt(self, prompt):
        self.server.handle_prompt(prompt)

📚 Documentation

Project Overview

Project Background

The MCP (Model Context Protocol) is a universal AI interface protocol, similar to USB - C. The MCP server can be connected to AI systems (such as Claude Desktop or cloud applications) as an external device, enabling the function of "develop once, use everywhere". The MCP client, embedded in the AI host, is responsible for one - to - one interaction with the server via the MCP protocol. The MCP client can call tools, query resources, and insert prompts; while the MCP server exposes tools (controlled by the model: retrieval, database update, sending), resources (controlled by the application: database records, APIs), and prompts (controlled by the user: documents).

Project Objectives

The goal is to build an AI infrastructure based on MCP that connects AI models with real - time structured knowledge, expands their capabilities through custom tool execution, and enhances agent combination capabilities. The aim is to create a dynamic AI system with an adaptable and plug - in architecture that can be integrated into multiple hosts and evolved through tool registration and real - time discovery.

Project Architecture

MCP as a Universal AI Interface

MCP acts as an interoperability layer that allows external AI applications (such as Claude, Cursor, etc.) to interact with structured data sources and executable functions. It adopts an architecture similar to USB - C, where the MCP server can be connected to various AI systems as an external plug - in.

Roles of MCP Client and Server

• MCP Client (Embedded in the AI Host):
  • Request tools
  • Query resources
  • Process prompts
  • Serve as a bridge between the AI system and the MCP server
• MCP Server (Locally Implemented):
  • Expose tools (such as weather APIs) for dynamic invocation by AI clients
  • Provide resources (such as API responses, database query results)
  • Process prompts for structured user interaction

MCP Architecture and Workflow

MCP as a Universal AI Interface

MCP functions similarly to USB - C, allowing external AI applications to connect to various AI systems in the form of plugins. The MCP server can be an independent external device, providing specific functions for AI clients to call.

Interaction between MCP Client and Server

• MCP Client: Embedded in the AI host (such as Claude Desktop), it is responsible for initiating tool requests, querying resources, and processing prompts.
• MCP Server: A locally implemented server that exposes tools, resources, and prompts for AI clients to use.

Conclusion

This project builds an AI infrastructure based on MCP that connects AI models to real - time structured knowledge and expands their capabilities through custom tool execution. The goal is to create a dynamic AI system with an adaptable and plug - in architecture that can be integrated into multiple hosts and evolved through tool registration and real - time discovery.

Implementation Details

"Vibe" Code Development

See the code examples in the "💻 Usage Examples" section.

Image Descriptions

• Image 1: An overall architecture diagram of the MCP protocol, showing the interaction process between the client and the server.
• Image 2: A schematic diagram of MCP tool registration and invocation, showing how to dynamically register and call different functions.

Summary

Through the MCP protocol, we can implement a flexible and highly scalable AI system that supports real - time interaction, dynamic function expansion, and multi - layer agent collaboration. This architecture is suitable not only for individual development but also for the construction of enterprise - level AI platforms.

🔧 Technical Details

The MCP protocol acts as a bridge between AI models and structured knowledge. It enables seamless interaction between different components of an AI system. The multi - layer agent interaction design in the MCP architecture allows for more complex task handling. The future dynamic tool discovery feature will further enhance the system's flexibility and scalability. The use of Anthropic's MCP Inspector simplifies the development and debugging process, making it more efficient.

📄 License

No license information is provided in the original document, so this section is skipped.